A rank-based approach to evaluate a surrogate marker in a small sample setting.

ABSTRACT: In clinical studies of chronic diseases, the effectiveness of an intervention is often assessed using "high cost" outcomes that require long-term patient follow-up and/or are invasive to obtain. While much progress has been made in the development of statistical methods to identify surrogate markers, that is, measurements that could replace such costly outcomes, they are generally not applicable to studies with a small sample size. These methods either rely on nonparametric smoothing which requires a relatively large sample size or rely on strict model assumptions that are unlikely to hold in practice and empirically difficult to verify with a small sample size. In this paper, we develop a novel rank-based nonparametric approach to evaluate a surrogate marker in a small sample size setting

SUBMITTER: Parast L 

PROVIDER: S-EPMC10883071 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature